Our hybrid systems use a DNN-HMM model implemented in the Kaldi toolkit [1]} and trained using BBN's Sage system [2]}.
The input features are 40-dimensional mel-filter cepstrum coefficients (MFCC) features, along with a 100-dimension ivector.
The HMM structure is the chain structure used in [3]}, which defines a two-state model with a single self-loop.
The acoustic model generates output at a reduced frame rate of 33Hz, one prediction for every three frames.
